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The article explains a newly developed noniterative, interpolation-based super-resolution (SR) technique, which uses the sampling theory for deriving the required SR algorithm. The approach is shown to be highly beneficial, as it minimizes the aliasing in the images and is also easily scalable.

In this paper, we consider an interieave-division multiple-access (IDMA) system with multiple users transmitting over a shared additive white Gaussian noise channel. In an IDMA system, the information sequence of each user is encoded by a low-rate serially concatenated code consisting of a convolutional code and a repetition code. To reduce decoding complexity, it has been proposed to only decode the inner repetition code for the first few iterations before including the full concatenated code constraints into the decoding process. Here, we characterize this concept of switched decoding as the parallel decoding of two complementary punctured codes and propose a systematic design procedure based on the extrinsic information transfer chart and trajectory analyses, leading to the best decoding convergence-complexity tradeoffs. Index Terms--Iterative decoding, multiple access, multiuser decoding, transfer function analysis.

Decomposing iterative question formats into the ascending and descending sequences, makes the anomalies to occur only in ascending sequences. Based on robust model, these anomalies can be eliminated without the loss of the statistical efficiency of most iterative question formats.

Turbo-codes are decoded using iterative decoding algorithms with somewhat elusive convergence properties. In this paper, the modes of convergence observed in an extensive series of turbo-code simulations are categorized by examining the bit convergence behavior for each frame. Based on these results, a new method for decoder termination based on average log-likelihood ratios is presented and compared with other methods. As an application, a selective repeat automatic repeat request (ARQ) system is considered. Index Terms--Decoder convergence, iterative decoding, log-likelihood decoding, stopping criterion, turbo-codes.

This paper analyses the convergence behavior of the least-squares state estimator. It is shown that the standard algorithm based on the Gauss-Newton method ignores second-order information. Under some conditions this information is essential for a good convergence. Examples show that an algorithm based on Newton's method reveals a more robust character. Keywords: least-squares power-system state-estimation; convergence analysis; Gauss-Newton iteration process

Sequence-to-sequence transformations are examined as a means to accelerate the convergence of iterative finite-element methods; in particular, the Shanks transform, the scalar epsilon algorithm (SEA), and the vector epsilon algorithm (VEA) are applied to dynamic relaxation (DR) solutions. One of the features of these algorithms is that they operate directly on the solution iterates and, therefore, do not require modification of the finite-element code itself. The accelerators are applied to a linear spring system and a cantilever elastica. Nonlinear sequence-to-sequence transformations are found to accelerate convergence by a factor of more than 10 and, thus, to largely overcome a major drawback of iterative finite-element methods, namely their slow rate of convergence. The VEA transformation is especially stable, is invariant with respect to coordinate transformations, and is compatible with typical kinematic constraints. Therefore, it is well suited to the acceleration of finite-element solution iterates.

The Newton's and quasi-Newton's methods used in the solution of two-dimensional laminar compressible Navier-Stokes equations are evaluated. Both are methods are applied to two test cases and analyzed using a supercomputer to determine the schemes' respective computation time and memory requirements. Results show that that methods employing approximate Jacobians and Jacobian inverses do not exhibit quadratic convergence.

This paper demonstrates the utility of the iterative proportional fitting procedure (IPF) in generating disaggregated spatial data from aggregated data and evaluates the performance of the procedure. Estimates of individual level data created by IPF using data of equal-interval categories are reliable, but the performance of the estimation can be improved by increasing sample size. The improvement usually is enough to offset the increase in error created by other factors. If the two variables defining the cross-classification have a significant interaction effect and the number of categories in each variable is larger than two, then IPF is preferred over an independent model. Key Words: IPF, census data, random error effect.

A new hybrid method for automatic clustering of satellite-observed natural scenes is presented. It uses a partitional clustering algorithm augmented by a hierarchical split-and-merge step at each iteration. The method also dynamically computes image-specific split-and-merge thresholds and can accommodate arbitrary information vectors. Better partitioning of the data and improved computational efficiency are achieved. The sensitivity of the method to the clustering parameters is presented, and the results show that the method is relatively insensitive to the choice of clustering parameters. Comparisons with the often used ISODATA algorithm show the effectiveness of the new approach. Index Terms--Clustering, image classification, remote sensing.

This paper presents an iterative detector that, by thresholding the output of the matched filter, gives maximum-likelihood sequence detector (MLSD) decisions on binary, antipodally modulated symbols that have been corrupted by intersymbol interference and additive Gaussian noise. The detector will make decisions on some, but often not all, of the symbols in a transmitted sequence, and those decisions will be the same decisions as the MLSD would have made. The number of symbols that are detected is stochastic, varying from sequence to sequence. The basis of the detector is a bound on a crossterm of the quadratic form in the log-likelihood function for the transmitted sequence. The detector is simple in structure, consisting of a matched filter and two variable threshold values for each symbol. Index Terms--Digital communication, equalizers, intersymbol interference, iterative methods, receiver.

We propose a novel hybrid aggressive space-mapping (HASM) optimization algorithm. HASM exploits both the trust-region aggressive space-mapping (TRASM) strategy and direct optimization. Severe differences between the coarse and fine models and nonuniqueness of the parameter extraction procedure may cause the TRASM algorithm to be trapped in local minima. The HASM algorithm is based on a novel lemma that enables smooth switching from the TRASM optimization to direct optimization if the TRASM algorithm is not converging. It also enables switching back from direct optimization to the TRASM algorithm in a smooth way. The uniqueness of the extraction step is improved by utilizing a good starting point. The algorithm does not assume that the final space-mapped design is the true optimal design and is robust against severe misalignment between the coarse and fine models. The examples include a seven-section waveguide transformer, the design of an H-plane waveguide filter, and a double-folded stub filter. Index Terms--CAD, design automation, electromagnetic simulation, iterative methods, microstrip filters, optimization methods, space mapping, waveguide filters.

A study proposed an effective approach for reducing blocking effects in transform image coding. The method requires two projection operators and the theory of projection onto convex sets (POCS) to ensure convergence of the iteration. However, the algorithm that was used is not strongly related to the theory of POCS. An alternative formal basis for establishing convergence and a more flexible theory for explaining the potential applications of the algorithm are presented.

The purpose of this paper is to report on the convergence rates of two iterative matrix solution methods individually and then to combine the two methods into a hybrid scheme to achieve additional convergence rate benefits. One interative matrix solver investigated is the sparse iterative method (SIM) which is a stationary, Jacobi-like solver but uses a sparse and not a banded matrix, with matrix elements corresponding to strong interactions, rather than position in the matrix. In this paper, SIM is modified to include an adaptive relaxation scheme to improve its convergence speed and numerical stability. Another iterative scheme investigated is the nonstationary biconjugate gradient stabilized (BiCGSTAB) method. It is shown that the BiCGSTAB is considerably improved when the method is preconditioned by the sparse matrix used in the SIM method. Finally, a hybrid scheme is proposed which combines both SIM-AR and BiCGSTAB-precon and it is shown that the hybrid gives best results on the problems considered. Examples giving convergence time versus accuracy are presented for two problems: a wire-grid plate, and a wire-grid partial helicopter. Index Terms--Iterative methods, simulation software, sparse matrices.